""" Class-Attention in Image Transformers (CaiT)

Paper: 'Going deeper with Image Transformers' - https://arxiv.org/abs/2103.17239

Original code and weights from https://github.com/facebookresearch/deit, copyright below

"""
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
from copy import deepcopy

import torch
import torch.nn as nn
from functools import partial

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg, overlay_external_default_cfg
from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_
from .registry import register_model


__all__ = [
    "Cait",
    "ClassAttn",
    "LayerScaleBlockClassAttn",
    "LayerScaleBlock",
    "TalkingHeadAttn",
]


def _cfg(url="", **kwargs):
    return {
        "url": url,
        "num_classes": 1000,
        "input_size": (3, 384, 384),
        "pool_size": None,
        "crop_pct": 1.0,
        "interpolation": "bicubic",
        "fixed_input_size": True,
        "mean": IMAGENET_DEFAULT_MEAN,
        "std": IMAGENET_DEFAULT_STD,
        "first_conv": "patch_embed.proj",
        "classifier": "head",
        **kwargs,
    }


default_cfgs = dict(
    cait_xxs24_224=_cfg(
        url="https://dl.fbaipublicfiles.com/deit/XXS24_224.pth",
        input_size=(3, 224, 224),
    ),
    cait_xxs24_384=_cfg(
        url="https://dl.fbaipublicfiles.com/deit/XXS24_384.pth",
    ),
    cait_xxs36_224=_cfg(
        url="https://dl.fbaipublicfiles.com/deit/XXS36_224.pth",
        input_size=(3, 224, 224),
    ),
    cait_xxs36_384=_cfg(
        url="https://dl.fbaipublicfiles.com/deit/XXS36_384.pth",
    ),
    cait_xs24_384=_cfg(
        url="https://dl.fbaipublicfiles.com/deit/XS24_384.pth",
    ),
    cait_s24_224=_cfg(
        url="https://dl.fbaipublicfiles.com/deit/S24_224.pth",
        input_size=(3, 224, 224),
    ),
    cait_s24_384=_cfg(
        url="https://dl.fbaipublicfiles.com/deit/S24_384.pth",
    ),
    cait_s36_384=_cfg(
        url="https://dl.fbaipublicfiles.com/deit/S36_384.pth",
    ),
    cait_m36_384=_cfg(
        url="https://dl.fbaipublicfiles.com/deit/M36_384.pth",
    ),
    cait_m48_448=_cfg(
        url="https://dl.fbaipublicfiles.com/deit/M48_448.pth",
        input_size=(3, 448, 448),
    ),
)


class ClassAttn(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications to do CA
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim**-0.5

        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.k = nn.Linear(dim, dim, bias=qkv_bias)
        self.v = nn.Linear(dim, dim, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        q = (
            self.q(x[:, 0])
            .unsqueeze(1)
            .reshape(B, 1, self.num_heads, C // self.num_heads)
            .permute(0, 2, 1, 3)
        )
        k = (
            self.k(x)
            .reshape(B, N, self.num_heads, C // self.num_heads)
            .permute(0, 2, 1, 3)
        )

        q = q * self.scale
        v = (
            self.v(x)
            .reshape(B, N, self.num_heads, C // self.num_heads)
            .permute(0, 2, 1, 3)
        )

        attn = q @ k.transpose(-2, -1)
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x_cls = (attn @ v).transpose(1, 2).reshape(B, 1, C)
        x_cls = self.proj(x_cls)
        x_cls = self.proj_drop(x_cls)

        return x_cls


class LayerScaleBlockClassAttn(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications to add CA and LayerScale
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        attn_block=ClassAttn,
        mlp_block=Mlp,
        init_values=1e-4,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = attn_block(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            attn_drop=attn_drop,
            proj_drop=drop,
        )
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = mlp_block(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )
        self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
        self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)

    def forward(self, x, x_cls):
        u = torch.cat((x_cls, x), dim=1)
        x_cls = x_cls + self.drop_path(self.gamma_1 * self.attn(self.norm1(u)))
        x_cls = x_cls + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x_cls)))
        return x_cls


class TalkingHeadAttn(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications to add Talking Heads Attention (https://arxiv.org/pdf/2003.02436v1.pdf)
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0):
        super().__init__()

        self.num_heads = num_heads

        head_dim = dim // num_heads

        self.scale = head_dim**-0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)

        self.proj = nn.Linear(dim, dim)

        self.proj_l = nn.Linear(num_heads, num_heads)
        self.proj_w = nn.Linear(num_heads, num_heads)

        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        qkv = (
            self.qkv(x)
            .reshape(B, N, 3, self.num_heads, C // self.num_heads)
            .permute(2, 0, 3, 1, 4)
        )
        q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]

        attn = q @ k.transpose(-2, -1)

        attn = self.proj_l(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)

        attn = attn.softmax(dim=-1)

        attn = self.proj_w(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class LayerScaleBlock(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications to add layerScale
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        attn_block=TalkingHeadAttn,
        mlp_block=Mlp,
        init_values=1e-4,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = attn_block(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            attn_drop=attn_drop,
            proj_drop=drop,
        )
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = mlp_block(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )
        self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
        self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)

    def forward(self, x):
        x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
        x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
        return x


class Cait(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications to adapt to our cait models
    def __init__(
        self,
        img_size=224,
        patch_size=16,
        in_chans=3,
        num_classes=1000,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4.0,
        qkv_bias=True,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),
        global_pool=None,
        block_layers=LayerScaleBlock,
        block_layers_token=LayerScaleBlockClassAttn,
        patch_layer=PatchEmbed,
        act_layer=nn.GELU,
        attn_block=TalkingHeadAttn,
        mlp_block=Mlp,
        init_scale=1e-4,
        attn_block_token_only=ClassAttn,
        mlp_block_token_only=Mlp,
        depth_token_only=2,
        mlp_ratio_clstk=4.0,
    ):
        super().__init__()

        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim

        self.patch_embed = patch_layer(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
        )

        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [drop_path_rate for i in range(depth)]
        self.blocks = nn.ModuleList(
            [
                block_layers(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[i],
                    norm_layer=norm_layer,
                    act_layer=act_layer,
                    attn_block=attn_block,
                    mlp_block=mlp_block,
                    init_values=init_scale,
                )
                for i in range(depth)
            ]
        )

        self.blocks_token_only = nn.ModuleList(
            [
                block_layers_token(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio_clstk,
                    qkv_bias=qkv_bias,
                    drop=0.0,
                    attn_drop=0.0,
                    drop_path=0.0,
                    norm_layer=norm_layer,
                    act_layer=act_layer,
                    attn_block=attn_block_token_only,
                    mlp_block=mlp_block_token_only,
                    init_values=init_scale,
                )
                for i in range(depth_token_only)
            ]
        )

        self.norm = norm_layer(embed_dim)

        self.feature_info = [dict(num_chs=embed_dim, reduction=0, module="head")]
        self.head = (
            nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
        )

        trunc_normal_(self.pos_embed, std=0.02)
        trunc_normal_(self.cls_token, std=0.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {"pos_embed", "cls_token"}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=""):
        self.num_classes = num_classes
        self.head = (
            nn.Linear(self.num_features, num_classes)
            if num_classes > 0
            else nn.Identity()
        )

    def forward_features(self, x):
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand(B, -1, -1)

        x = x + self.pos_embed
        x = self.pos_drop(x)

        for i, blk in enumerate(self.blocks):
            x = blk(x)

        for i, blk in enumerate(self.blocks_token_only):
            cls_tokens = blk(x, cls_tokens)

        x = torch.cat((cls_tokens, x), dim=1)

        x = self.norm(x)
        return x[:, 0]

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x


def checkpoint_filter_fn(state_dict, model=None):
    if "model" in state_dict:
        state_dict = state_dict["model"]
    checkpoint_no_module = {}
    for k, v in state_dict.items():
        checkpoint_no_module[k.replace("module.", "")] = v
    return checkpoint_no_module


def _create_cait(variant, pretrained=False, **kwargs):
    if kwargs.get("features_only", None):
        raise RuntimeError(
            "features_only not implemented for Vision Transformer models."
        )

    model = build_model_with_cfg(
        Cait,
        variant,
        pretrained,
        default_cfg=default_cfgs[variant],
        pretrained_filter_fn=checkpoint_filter_fn,
        **kwargs
    )
    return model


@register_model
def cait_xxs24_224(pretrained=False, **kwargs):
    model_args = dict(
        patch_size=16, embed_dim=192, depth=24, num_heads=4, init_scale=1e-5, **kwargs
    )
    model = _create_cait("cait_xxs24_224", pretrained=pretrained, **model_args)
    return model


@register_model
def cait_xxs24_384(pretrained=False, **kwargs):
    model_args = dict(
        patch_size=16, embed_dim=192, depth=24, num_heads=4, init_scale=1e-5, **kwargs
    )
    model = _create_cait("cait_xxs24_384", pretrained=pretrained, **model_args)
    return model


@register_model
def cait_xxs36_224(pretrained=False, **kwargs):
    model_args = dict(
        patch_size=16, embed_dim=192, depth=36, num_heads=4, init_scale=1e-5, **kwargs
    )
    model = _create_cait("cait_xxs36_224", pretrained=pretrained, **model_args)
    return model


@register_model
def cait_xxs36_384(pretrained=False, **kwargs):
    model_args = dict(
        patch_size=16, embed_dim=192, depth=36, num_heads=4, init_scale=1e-5, **kwargs
    )
    model = _create_cait("cait_xxs36_384", pretrained=pretrained, **model_args)
    return model


@register_model
def cait_xs24_384(pretrained=False, **kwargs):
    model_args = dict(
        patch_size=16, embed_dim=288, depth=24, num_heads=6, init_scale=1e-5, **kwargs
    )
    model = _create_cait("cait_xs24_384", pretrained=pretrained, **model_args)
    return model


@register_model
def cait_s24_224(pretrained=False, **kwargs):
    model_args = dict(
        patch_size=16, embed_dim=384, depth=24, num_heads=8, init_scale=1e-5, **kwargs
    )
    model = _create_cait("cait_s24_224", pretrained=pretrained, **model_args)
    return model


@register_model
def cait_s24_384(pretrained=False, **kwargs):
    model_args = dict(
        patch_size=16, embed_dim=384, depth=24, num_heads=8, init_scale=1e-5, **kwargs
    )
    model = _create_cait("cait_s24_384", pretrained=pretrained, **model_args)
    return model


@register_model
def cait_s36_384(pretrained=False, **kwargs):
    model_args = dict(
        patch_size=16, embed_dim=384, depth=36, num_heads=8, init_scale=1e-6, **kwargs
    )
    model = _create_cait("cait_s36_384", pretrained=pretrained, **model_args)
    return model


@register_model
def cait_m36_384(pretrained=False, **kwargs):
    model_args = dict(
        patch_size=16, embed_dim=768, depth=36, num_heads=16, init_scale=1e-6, **kwargs
    )
    model = _create_cait("cait_m36_384", pretrained=pretrained, **model_args)
    return model


@register_model
def cait_m48_448(pretrained=False, **kwargs):
    model_args = dict(
        patch_size=16, embed_dim=768, depth=48, num_heads=16, init_scale=1e-6, **kwargs
    )
    model = _create_cait("cait_m48_448", pretrained=pretrained, **model_args)
    return model
